Hysteresis-based design of dynamic reference trajectories to avoid saturation in controlled wind turbines

The main objective of this paper is to design a dynamic reference trajectory based on hysteresis to avoid saturation in controlled wind turbines. Basically, the torque controller and pitch controller set-points are hysteretically manipulated to avoid saturation and drive the system with smooth dynamic changes. Simulation results obtained from a 5MW wind turbine benchmark model show that our proposed strategy has a clear added value with respect to the baseline controller (a well-known and accepted industrial wind turbine controller). Moreover, the proposed strategy has been tested in healthy conditions but also in the presence of a realistic fault where the baseline controller caused saturation to nally conduct to instability.Peer ReviewedPostprint (author's final draft

Comparison of Mixed H 2 H∞ with Regional Pole Placement Control and H 2 Optimal Control for the Design of Steam Condenser

This paper investigates the comparison between mixed H 2 /H∞ with regional pole placement control and H 2 optimal control for the design of steam condenser. The comparison have been made for a step change in the steam condenser pressure set point for a step change of 10 & 23 seconds using MATLAB/Simulink environment for the steam condenser with mixed H 2 /H∞ with regional pole placement controller, steam condenser with H 2 optimal controller and steam condenser without controller. The steam condenser with mixed H 2 /H∞ with regional pole placement controller presented excellent and superior dynamic performance in response to the two step changes and an improvement in settling time. The overall simulation results demonstrated that the steam condenser with mixed H 2 /H∞ with regional pole placement controller can be an efficient alternative to the steam condenser with H 2 optimal controller for the steam condenser

PID and PID-like controller design by pole assignment within D-stable regions

This paper presents a new PID and PID-like controller design method that permits the designer to control the desired dynamic performance of a closed-loop system by first specifying a set of desired D-stable regions in the complex plane and then running a numerical optimisation algorithm to find the controller parameters such that all the roots of the closed-loop system are within the specified regions. This method can be used for stable and unstable plants with high order degree, for plants with time delay, for controller with more than three design parameters, and for various controller configurations. It also allows a unified treatment of the controller design for both continuous and discrete systems. Examples and comparative simulation results are pro-vided to illustrate its merit

Adaptive signal control using approximate dynamic programming

This paper presents a concise summary of a study on adaptive traffic signal controller for real time operation. The adaptive controller is designed to achieve three operational objectives: first, the controller adopts a dual control principle to achieve a balanced influence between immediate cost and long-term cost in operation; second, controller switches signals without referring to a preset plan and is acyclic; third, controller adjusts its parameters online to adapt new environment. Not all of these features are available in existing operational controllers. Although dynamic programming (DP) is the only exact solution for achieving the operational objectives, it is usually impractical for real time operation because of demand in computation and information. To circumvent the difficulties, we use approximate dynamic programming (ADP) in conjunction with online learning techniques. This approach can substantially reduce computational burden by replacing the exact value function of DP with a continuous linear approximation function, which is then updated progressively by online learning techniques. Two online learning techniques, which are reinforcement learning and monotonicity approximation respectively, are investigated. We find in computer simulation that the ADP controller leads to substantial savings in vehicle delays in comparison with optimised fixed-time plans. The implications of this study to traffic control are: the ADP controller meet all of the three operational objectives with competitive results, and can be readily implemented for operations at both isolated intersection and traffic networks; the ADP algorithm is computationally efficient, and the ADP controller is an evolving system that requires minimum human intervention; the ADP technique offers a flexible theoretical framework in which a range of functional forms and learning techniques can be further studied